Date: (Mon) Jun 13, 2016

Introduction:

Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
  • fit.models chunk:
    • Classification: Plot AUC Curves for all models & highlight glbMdlSel
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
    debugSource("~/Dropbox/datascience/R/mydsutils.R") else
    source("~/Dropbox/datascience/R/mydsutils.R")    
## Loading required package: caret
## Loading required package: lattice
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
require(stringr)
## Loading required package: stringr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>"; 
#               or named collection of <PathPointer>s
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
    # or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
    #, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
    #                       select from c("copy", NULL ???, "condition", "sample", )
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    )                   
 
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv") 

glbObsDropCondition <- #NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # 'is.na(glbObsAll[, glb_rsp_var_raw])'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
    # 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
# '(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))' # No
'(glbObsAll[, "Q109244"] != "")' # NA
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "Party"

# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
#     
# chk ref value against frequencies vs. alpha sort order
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D")) 
    
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))

glb_map_rsp_var_to_raw <- #NULL 
function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
#     levels(var)[as.numeric(var)]
    sapply(levels(var)[as.numeric(var)], function(elm) 
        if (is.na(elm)) return(elm) else
        if (elm == 'R') return("Republican") else
        if (elm == 'D') return("Democrat") else
        stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
        )  
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>") 
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q115611.fctr" # choose from c(NULL : default, "<category_feat>")

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & 
#       work each one in
    , "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel" 
    ,"Q124742","Q124122" 
    ,"Q123621","Q123464"
    ,"Q122771","Q122770","Q122769","Q122120"
    ,"Q121700","Q121699","Q121011"
    ,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012" 
    ,"Q119851","Q119650","Q119334"
    ,"Q118892","Q118237","Q118233","Q118232","Q118117"
    ,"Q117193","Q117186"
    ,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
    ,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
    ,"Q114961","Q114748","Q114517","Q114386","Q114152"
    ,"Q113992","Q113583","Q113584","Q113181"
    ,"Q112478","Q112512","Q112270"
    ,"Q111848","Q111580","Q111220"
    ,"Q110740"
    ,"Q109367","Q109244"
    ,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
    ,"Q107869","Q107491"
    ,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
    ,"Q105840","Q105655"
    ,"Q104996"
    ,"Q103293"
    ,"Q102906","Q102674","Q102687","Q102289","Q102089"
    ,"Q101162","Q101163","Q101596"
    ,"Q100689","Q100680","Q100562","Q100010"
    ,"Q99982"
    ,"Q99716"
    ,"Q99581"
    ,"Q99480"
    ,"Q98869"
    ,"Q98578"
    ,"Q98197"
    ,"Q98059","Q98078"
    ,"Q96024" # Done
    ,".pos") 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) } 
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }
#     mapfn = function(Name) { return(sapply(Name, function(thsName) 
#                                             str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) } 

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(raw1) { return(1:length(raw1)) }
    , args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
#     mapfn = function(raw1) { return(1:length(raw1)) }       
#     , args = c(".rnorm"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
#     mapfn = function(District) {
#         raw <- District;
#         ret_vals <- rep_len("NA", length(raw)); 
#         ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm) 
#                                         ifelse(elm < 10, "1-9", 
#                                         ifelse(elm < 20, "10-19", "20+")));
#         return(relevel(as.factor(ret_vals), ref = "NA"))
#     }       
#     , args = c("District"))    

# YOB options:
# 1. Missing data:
# 1.1   0 -> Does not improve baseline
# 1.2   Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        # raw[!is.na(raw) & raw >= 2010] <- NA
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        retVal <- rep_len("NA", length(raw))
        # breaks = c(1879, seq(1949, 1989, 10), 2049)
        # cutVal <- cut(raw[!is.na(raw)], breaks = breaks, 
        #               labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
        cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
        retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
        return(factor(retVal, levels = c("NA"
                ,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
                        ordered = TRUE))
    }
    , args = c("YOB"))

# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)

        # retVal <- rep_len(0, length(raw))
        stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
        stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0) 
        # msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
        # msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
        # msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
        # msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
        # msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
        # msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
        # msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
        # msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65

        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)        
        retVal <- sapply(raw, function(age) {
            if (is.na(age)) return(0) else
            if ((age > 15) && (age <= 20)) return(age - 15) else
            if ((age > 20) && (age <= 25)) return(age - 20) else
            if ((age > 25) && (age <= 30)) return(age - 25) else
            if ((age > 30) && (age <= 35)) return(age - 30) else
            if ((age > 35) && (age <= 40)) return(age - 35) else
            if ((age > 40) && (age <= 50)) return(age - 40) else
            if ((age > 50) && (age <= 65)) return(age - 50) else
            if ((age > 65) && (age <= 90)) return(age - 65)
        })
        
        return(retVal)
    }
    , args = c("YOB"))

glbFeatsDerive[["Gender.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- raw1
        raw[raw %in% ""] <- "N"
        raw <- gsub("Male"  , "M", raw, fixed = TRUE)
        raw <- gsub("Female", "F", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("Gender"))

glbFeatsDerive[["Income.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("under $25,000"      , "<25K"    , raw, fixed = TRUE)
        raw <- gsub("$25,001 - $50,000"  , "25-50K"  , raw, fixed = TRUE)
        raw <- gsub("$50,000 - $74,999"  , "50-75K"  , raw, fixed = TRUE)
        raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)        
        raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
        raw <- gsub("over $150,000"      , ">150K"   , raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
                      ordered = TRUE))
    }
    , args = c("Income"))

glbFeatsDerive[["Hhold.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
        raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)        
        raw <- gsub("Married (no kids)"          , "MKn", raw, fixed = TRUE)
        raw <- gsub("Married (w/kids)"           , "MKy", raw, fixed = TRUE)        
        raw <- gsub("Single (no kids)"           , "SKn", raw, fixed = TRUE)
        raw <- gsub("Single (w/kids)"            , "SKy", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("HouseholdStatus"))

glbFeatsDerive[["Edn.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Current K-12"         , "K12", raw, fixed = TRUE)
        raw <- gsub("High School Diploma"  , "HSD", raw, fixed = TRUE)        
        raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
        raw <- gsub("Associate's Degree"   , "Ast", raw, fixed = TRUE)
        raw <- gsub("Bachelor's Degree"    , "Bcr", raw, fixed = TRUE)        
        raw <- gsub("Master's Degree"      , "Msr", raw, fixed = TRUE)
        raw <- gsub("Doctoral Degree"      , "PhD", raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
                      ordered = TRUE))
    }
    , args = c("EducationLevel"))

# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))    
    glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
        mapfn = function(raw1) {
            raw1[raw1 %in% ""] <- "NA"
            rawVal <- unique(raw1)
            
            if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
                raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
                raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
                raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
                raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
                raw1 <- gsub("Idealist"  , "Id", raw1, fixed = TRUE)
                raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
                raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
                raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            }
            
            return(relevel(as.factor(raw1), ref = "NA"))
        }
        , args = c(qsn))

# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
#     mapfn = function(FertilityRate, Region) {
#         RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
# 
#         retVal <- FertilityRate
#         retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
#         return(retVal)
#     }
#     , args = c("FertilityRate", "Region"))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE, 
#       last.ctg = FALSE, poly.ctg = FALSE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr", 
                     # # "Hhold.fctr",
                     # "Edn.fctr",
                     # paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[", 
#                         toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
#                                       "]\\.[PT]\\."), 
#                                names(glbObsAll), value = TRUE)

glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(2, 3, 4, 5, 6, 7, 8, 16, 32, 64, 128, 247) # accuracy(5) = 0.6154
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164

glbRFEResults <- NULL

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()

# Add xgboost algorithm

# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
            ,"xgbLinear","xgbTree"
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            ,"xgbLinear","xgbTree"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart","xgbLinear","xgbTree"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
#   RFE = "Recursive Feature Elimination"
#   Csm = CuStoM
#   NOr = No OutlieRs
#   Inc = INteraCt
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
    # glm does not work for multinomial
    glbMdlFamilies[["All.X"]] <- c("glmnet") 
} else {
    # glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
    glbMdlFamilies[["All.X"]] <- c("glmnet")
    # glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
    # glbMdlFamilies[["RFE.X"]] <- c("glmnet")    
    # glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
    #     # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
    #     # , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
    #     , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
    #     , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
    #     , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
    #     , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
    #     ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
    #                                     ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()

# When glmnet crashes at model$grid with error: ???
AllX__rcv_glmnetTuneParams <- rbind(data.frame()
    ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
    ,data.frame(parameter = "lambda", vals = "0.0053781495 0.01 0.0249631588 0.03 0.04454817")
                        ) # max.Accuracy.OOB = 0.5981941 @ 0.775 0.02496316

glbMdlTuneParams <- rbind(glbMdlTuneParams
    ,cbind(data.frame(mdlId = "All.X##rcv#glmnet"),            AllX__rcv_glmnetTuneParams)
)

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# bagEarthTuneParams <- rbind(data.frame()
#                         ,data.frame(parameter = "degree", vals = "1")
#                         ,data.frame(parameter = "nprune", vals = "256")
#                         )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "Final.RFE.X.Inc##rcv#bagEarth"),
#                                      bagEarthTuneParams))

# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)

pkgPreprocMethods <-     
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
#   Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
    c(NULL
      ,"zv", "nzv"
      ,"BoxCox", "YeoJohnson", "expoTrans"
      ,"center", "scale", "center.scale", "range"
      ,"knnImpute", "bagImpute", "medianImpute"
      ,"zv.pca", "ica", "spatialSign"
      ,"conditionalX") 

glbMdlPreprocMethods <- list(NULL# NULL # : default
    # ,"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
    #                                         c("knnImpute", "bagImpute", "medianImpute")),
    #                                 # c(NULL)))
    #                                 c("zv.pca.spatialSign")))
    # ,"RFE.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
    #                                         c("knnImpute", "bagImpute", "medianImpute")),
    #                                 c(NULL)))
    #                                 # c("zv.pca.spatialSign")))
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
#                                                     "nzv.pca.spatialSign"))

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
                           "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
              "min.elapsedtime.everything", 
              # "min.aic.fit", 
              "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL # NULL : default #"auto"
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )
glbMdlEnsembleSampleMethods <- c("boot", "boot632", "cv", "repeatedcv"
               # , "LOOCV" # tuneLength * nrow(fitDF) # way too many models
               , "LGOCV"
               , "adaptive_cv" # crashed for Q109244No
               # , "adaptive_boot"  #error: adaptive$min should be less than 3
               # , "adaptive_LGOCV" #error: adaptive$min should be less than 3
               )


# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glbMdlSelId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
#     require(tidyr)
#     obsOutFinDf <- obsOutFinDf %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     # mnm prefix stands for max_n_mean
#     mnmout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         #dplyr::top_n(1, Probability1) %>% # Score = 3.9426         
#         #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;         
#         #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169; 
#         dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;        
#         #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))    
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
#         dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), 
#                          yMeanN = weighted.mean(as.numeric(y), c(Probability1)))  
#     
#     maxout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         dplyr::summarize(maxProb1 = max(Probability1))
#     fltout_df <- merge(maxout_df, obsOutFinDf, 
#                        by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
#                        all.x = TRUE)
#     fmnout_df <- merge(fltout_df, mnmout_df, 
#                        by.x = c(".pos"), by.y = c(".pos"),
#                        all.x = TRUE)
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsOutFinDf) {
#                   }
                  )
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
#     txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
#         dplyr::mutate(
#             lunch     = levels(glbObsTrn[, "lunch"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
#             dinner    = levels(glbObsTrn[, "dinner"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
#             reserve   = levels(glbObsTrn[, "reserve"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "outdoor"  ])), 0)],
#             expensive = levels(glbObsTrn[, "expensive"])[
#                        round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
#             liquor    = levels(glbObsTrn[, "liquor"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
#             table     = levels(glbObsTrn[, "table"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
#             classy    = levels(glbObsTrn[, "classy"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
#             kids      = levels(glbObsTrn[, "kids"     ])[
#                        round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
#                       )
#     
#     print("ObsNew output class tables:")
#     print(sapply(c("lunch","dinner","reserve","outdoor",
#                    "expensive","liquor","table",
#                    "classy","kids"), 
#                  function(feat) table(txfout_df[, feat], useNA = "ifany")))
#     
#     txfout_df <- txfout_df %>%
#         dplyr::mutate(labels = "") %>%
#         dplyr::mutate(labels = 
#     ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
#         dplyr::select(business_id, labels)
#     return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    # glbObsOut$vars[["Probability1"]] <- 
    #     "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]" 
    # glbObsOut$vars[[glb_rsp_var_raw]] <-
    #     "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
    #                                         mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
    glbObsOut$vars[["Predictions"]] <-
        "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
                                            mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- # NULL #: default
    c("Q109244No_AllXpreProc_cnk03_rest_out_fin.csv") 
    # c("Votes_Ensemble_cnk06_out_fin.csv") 


glbOut <- list(pfx = "Q109244NA_AllX_cnk01_rest_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- "fit.models_1" # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL # default: script will save envir at end of this chunk 
glbChunks[["inpFilePathName"]] <- "data/Q109244NA_AllX_cnk01_fit.models_1_fit.models_1.RData" # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, 
                             ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))
##          label step_major step_minor label_minor   bgn end elapsed
## 1 fit.models_1          1          0           0 5.813  NA      NA

Step 1.0: fit models_1

chunk option: eval=

Step 1.0: fit models_1

Step 1.0: fit models_1

```{r scrub.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

Step 1.0: fit models_1

```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: fit models_1

Step 1.0: fit models_1

```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: fit models_1

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
# load(paste0(glbOut$pfx, "dsk.RData"))

glbgetModelSelectFormula <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

glbgetDisplayModelsDf <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#glbgetDisplayModelsDf()

glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
# c("id.prefix", "method", "type",
#   # trainControl params
#   "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
#   # train params
#   "metric", "metric.maximize", "tune.df")

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indepVar=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indepVar = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indepVar = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")

ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
    train.method = "glmnet")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)

if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")
indepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVar, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")

rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)

```{r fit.models_1, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}

##              label step_major step_minor label_minor   bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 8.955  NA      NA
##                label step_major step_minor label_minor   bgn   end elapsed
## 1   fit.models_1_bgn          1          0       setup 8.955 8.964   0.009
## 2 fit.models_1_All.X          1          1       setup 8.964    NA      NA
##                label step_major step_minor label_minor   bgn  end elapsed
## 2 fit.models_1_All.X          1          1       setup 8.964 8.97   0.006
## 3 fit.models_1_All.X          1          2      glmnet 8.971   NA      NA
## [1] "skipping fitting model: All.X##rcv#glmnet"
##                  label step_major step_minor label_minor   bgn   end
## 3   fit.models_1_All.X          1          2      glmnet 8.971 8.977
## 4 fit.models_1_preProc          1          3     preProc 8.978    NA
##   elapsed
## 3   0.006
## 4      NA
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last
## The following object is masked from 'package:stats':
## 
##     nobs
## The following object is masked from 'package:utils':
## 
##     object.size
##                                 min.elapsedtime.everything
## Random###myrandom_classfr                            0.301
## MFO###myMFO_classfr                                  0.455
## Max.cor.Y.rcv.1X1###glmnet                           0.796
## Max.cor.Y##rcv#rpart                                 1.403
## Interact.High.cor.Y##rcv#glmnet                      2.698
## Low.cor.X##rcv#glmnet                                8.077
## All.X##rcv#glmnet                                    9.352
##                  label step_major step_minor label_minor   bgn   end
## 4 fit.models_1_preProc          1          3     preProc 8.978 9.415
## 5     fit.models_1_end          1          4    teardown 9.416    NA
##   elapsed
## 4   0.437
## 5      NA
##          label step_major step_minor label_minor   bgn   end elapsed
## 1 fit.models_1          1          0           0 5.813 9.421   3.608
## 2   fit.models          1          1           1 9.421    NA      NA

```{r fit.models_2, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}

##              label step_major step_minor label_minor   bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 9.495  NA      NA
## Loading required package: reshape2
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
## Loading required package: RColorBrewer
## Warning: Removed 3 rows containing missing values (geom_errorbar).
## quartz_off_screen 
##                 2
## Warning: Removed 3 rows containing missing values (geom_errorbar).

##                                id max.Accuracy.OOB max.AUCROCR.OOB
## 7               All.X##rcv#glmnet        0.5981941       0.6177236
## 6           Low.cor.X##rcv#glmnet        0.5801354       0.6063250
## 5 Interact.High.cor.Y##rcv#glmnet        0.5733634       0.5847057
## 3      Max.cor.Y.rcv.1X1###glmnet        0.5485327       0.5505203
## 4            Max.cor.Y##rcv#rpart        0.5440181       0.5511143
## 2       Random###myrandom_classfr        0.5349887       0.5054791
## 1             MFO###myMFO_classfr        0.5349887       0.5000000
##   max.AUCpROC.OOB min.elapsedtime.everything max.Accuracy.fit
## 7       0.5514420                      9.352        0.5764853
## 6       0.5447954                      8.077        0.5764843
## 5       0.5682479                      2.698        0.5540889
## 3       0.5471714                      0.796        0.5588742
## 4       0.5471714                      1.403        0.5588680
## 2       0.5555897                      0.301        0.5364733
## 1       0.5000000                      0.455        0.5364733
##   opt.prob.threshold.fit opt.prob.threshold.OOB
## 7                   0.50                   0.45
## 6                   0.45                   0.45
## 5                   0.50                   0.50
## 3                   0.50                   0.55
## 4                   0.50                   0.50
## 2                   0.55                   0.55
## 1                   0.50                   0.50
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB + min.elapsedtime.everything - 
##     max.Accuracy.fit - opt.prob.threshold.OOB
## <environment: 0x7fba1e8d73c0>
## [1] "Best model id: All.X##rcv#glmnet"
## glmnet 
## 
## 1741 samples
##  108 predictor
##    2 classes: 'D', 'R' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold, repeated 3 times) 
## Summary of sample sizes: 1161, 1160, 1161, 1161, 1160, 1161, ... 
## Resampling results across tuning parameters:
## 
##   alpha  lambda        Accuracy   Kappa     
##   0.100  0.0000537815  0.5458573  0.08481791
##   0.100  0.0002496316  0.5452826  0.08358586
##   0.100  0.0011586872  0.5449001  0.08250874
##   0.100  0.0053781495  0.5494919  0.09112777
##   0.100  0.0249631588  0.5550415  0.09967103
##   0.325  0.0000537815  0.5456661  0.08430186
##   0.325  0.0002496316  0.5458570  0.08445865
##   0.325  0.0011586872  0.5441335  0.08096979
##   0.325  0.0053781495  0.5519777  0.09551564
##   0.325  0.0249631588  0.5621286  0.11002394
##   0.550  0.0000537815  0.5456654  0.08419681
##   0.550  0.0002496316  0.5448991  0.08245142
##   0.550  0.0011586872  0.5468129  0.08625734
##   0.550  0.0053781495  0.5500636  0.09102968
##   0.550  0.0249631588  0.5741904  0.12914618
##   0.775  0.0000537815  0.5452823  0.08337583
##   0.775  0.0002496316  0.5445170  0.08172868
##   0.775  0.0011586872  0.5481509  0.08867988
##   0.775  0.0053781495  0.5538918  0.09782035
##   0.775  0.0249631588  0.5764853  0.12735917
##   1.000  0.0000537815  0.5454738  0.08373635
##   1.000  0.0002496316  0.5449001  0.08249287
##   1.000  0.0011586872  0.5483418  0.08884247
##   1.000  0.0053781495  0.5523628  0.09388169
##   1.000  0.0249631588  0.5674844  0.10062194
## 
## Accuracy was used to select the optimal model using  the largest value.
## The final values used for the model were alpha = 0.775 and lambda
##  = 0.02496316.
## [1] "All.X##rcv#glmnet fit prediction diagnostics:"
## Loading required package: glmnet
## Loading required package: Matrix
## Loaded glmnet 2.0-5
## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"
##                                 All.X..rcv.glmnet.imp         imp
## Q113181.fctrYes                           100.0000000 100.0000000
## Q101163.fctrMom                            88.9098425  88.9098425
## Q115611.fctrYes                            82.6333748  82.6333748
## Gender.fctrM                               45.6503194  45.6503194
## Q98197.fctrNo                              41.3739497  41.3739497
## Hhold.fctrN:.clusterid.fctr4               39.7168373  39.7168373
## Q115611.fctrNo                             37.0447808  37.0447808
## Q120650.fctrNo                             33.9820515  33.9820515
## Hhold.fctrPKn                              33.2892285  33.2892285
## Q119851.fctrNo                             29.5349743  29.5349743
## Q108950.fctrRisk-friendly                  27.4476100  27.4476100
## Q102674.fctrYes                            26.3084312  26.3084312
## Q114386.fctrTMI                            26.0760004  26.0760004
## Q116441.fctrYes                            23.9332042  23.9332042
## Q98869.fctrNo                              23.4958757  23.4958757
## Q106389.fctrNo                             20.7891725  20.7891725
## Edn.fctr^4                                 19.8530900  19.8530900
## Q111848.fctrYes                            18.3541103  18.3541103
## Q116441.fctrNo                             18.1901772  18.1901772
## Q109367.fctrYes                            18.0382944  18.0382944
## YOB.Age.fctr^8                             17.5760073  17.5760073
## Q100562.fctrNo                             15.7468265  15.7468265
## Q116601.fctrNo                             14.6114962  14.6114962
## Q120379.fctrNo                             12.2533356  12.2533356
## Hhold.fctrPKy                               8.9203943   8.9203943
## Q113583.fctrTunes                           8.0266221   8.0266221
## Q120379.fctrYes                             6.9206106   6.9206106
## Edn.fctr.L                                  6.3143991   6.3143991
## Q122771.fctrPt                              3.4734080   3.4734080
## Q99480.fctrYes                              3.2766182   3.2766182
## Q115899.fctrCs                              2.9965936   2.9965936
## Q120012.fctrNo                              1.9105692   1.9105692
## Edn.fctr^7                                  1.8595355   1.8595355
## Q120472.fctrScience                         0.7386151   0.7386151
## Q101596.fctrYes                             0.4989205   0.4989205
## .rnorm                                      0.0000000   0.0000000
## Edn.fctr.Q                                  0.0000000   0.0000000
## Edn.fctr.C                                  0.0000000   0.0000000
## Edn.fctr^5                                  0.0000000   0.0000000
## Edn.fctr^6                                  0.0000000   0.0000000
## Gender.fctrF                                0.0000000   0.0000000
## Hhold.fctrMKn                               0.0000000   0.0000000
## Hhold.fctrMKy                               0.0000000   0.0000000
## Hhold.fctrSKn                               0.0000000   0.0000000
## Hhold.fctrSKy                               0.0000000   0.0000000
## Income.fctr.L                               0.0000000   0.0000000
## Income.fctr.Q                               0.0000000   0.0000000
## Income.fctr.C                               0.0000000   0.0000000
## Income.fctr^4                               0.0000000   0.0000000
## Income.fctr^5                               0.0000000   0.0000000
## Income.fctr^6                               0.0000000   0.0000000
## Q100010.fctrNo                              0.0000000   0.0000000
## Q100010.fctrYes                             0.0000000   0.0000000
## Q100562.fctrYes                             0.0000000   0.0000000
## Q100680.fctrNo                              0.0000000   0.0000000
## Q100680.fctrYes                             0.0000000   0.0000000
## Q100689.fctrNo                              0.0000000   0.0000000
## Q100689.fctrYes                             0.0000000   0.0000000
## Q101162.fctrOptimist                        0.0000000   0.0000000
## Q101162.fctrPessimist                       0.0000000   0.0000000
## Q101163.fctrDad                             0.0000000   0.0000000
## Q101596.fctrNo                              0.0000000   0.0000000
## Q102089.fctrOwn                             0.0000000   0.0000000
## Q102089.fctrRent                            0.0000000   0.0000000
## Q102289.fctrNo                              0.0000000   0.0000000
## Q102289.fctrYes                             0.0000000   0.0000000
## Q102674.fctrNo                              0.0000000   0.0000000
## Q102687.fctrNo                              0.0000000   0.0000000
## Q102687.fctrYes                             0.0000000   0.0000000
## Q102906.fctrNo                              0.0000000   0.0000000
## Q102906.fctrYes                             0.0000000   0.0000000
## Q103293.fctrNo                              0.0000000   0.0000000
## Q103293.fctrYes                             0.0000000   0.0000000
## Q104996.fctrNo                              0.0000000   0.0000000
## Q104996.fctrYes                             0.0000000   0.0000000
## Q105655.fctrNo                              0.0000000   0.0000000
## Q105655.fctrYes                             0.0000000   0.0000000
## Q105840.fctrNo                              0.0000000   0.0000000
## Q105840.fctrYes                             0.0000000   0.0000000
## Q106042.fctrNo                              0.0000000   0.0000000
## Q106042.fctrYes                             0.0000000   0.0000000
## Q106272.fctrNo                              0.0000000   0.0000000
## Q106272.fctrYes                             0.0000000   0.0000000
## Q106388.fctrNo                              0.0000000   0.0000000
## Q106388.fctrYes                             0.0000000   0.0000000
## Q106389.fctrYes                             0.0000000   0.0000000
## Q106993.fctrNo                              0.0000000   0.0000000
## Q106993.fctrYes                             0.0000000   0.0000000
## Q106997.fctrGr                              0.0000000   0.0000000
## Q106997.fctrYy                              0.0000000   0.0000000
## Q107491.fctrNo                              0.0000000   0.0000000
## Q107491.fctrYes                             0.0000000   0.0000000
## Q107869.fctrNo                              0.0000000   0.0000000
## Q107869.fctrYes                             0.0000000   0.0000000
## Q108342.fctrIn-person                       0.0000000   0.0000000
## Q108342.fctrOnline                          0.0000000   0.0000000
## Q108343.fctrNo                              0.0000000   0.0000000
## Q108343.fctrYes                             0.0000000   0.0000000
## Q108617.fctrNo                              0.0000000   0.0000000
## Q108617.fctrYes                             0.0000000   0.0000000
## Q108754.fctrNo                              0.0000000   0.0000000
## Q108754.fctrYes                             0.0000000   0.0000000
## Q108855.fctrUmm...                          0.0000000   0.0000000
## Q108855.fctrYes!                            0.0000000   0.0000000
## Q108856.fctrSocialize                       0.0000000   0.0000000
## Q108856.fctrSpace                           0.0000000   0.0000000
## Q108950.fctrCautious                        0.0000000   0.0000000
## Q109367.fctrNo                              0.0000000   0.0000000
## Q110740.fctrMac                             0.0000000   0.0000000
## Q110740.fctrPC                              0.0000000   0.0000000
## Q111220.fctrNo                              0.0000000   0.0000000
## Q111220.fctrYes                             0.0000000   0.0000000
## Q111580.fctrDemanding                       0.0000000   0.0000000
## Q111580.fctrSupportive                      0.0000000   0.0000000
## Q111848.fctrNo                              0.0000000   0.0000000
## Q112270.fctrNo                              0.0000000   0.0000000
## Q112270.fctrYes                             0.0000000   0.0000000
## Q112478.fctrNo                              0.0000000   0.0000000
## Q112478.fctrYes                             0.0000000   0.0000000
## Q112512.fctrNo                              0.0000000   0.0000000
## Q112512.fctrYes                             0.0000000   0.0000000
## Q113181.fctrNo                              0.0000000   0.0000000
## Q113583.fctrTalk                            0.0000000   0.0000000
## Q113584.fctrPeople                          0.0000000   0.0000000
## Q113584.fctrTechnology                      0.0000000   0.0000000
## Q113992.fctrNo                              0.0000000   0.0000000
## Q113992.fctrYes                             0.0000000   0.0000000
## Q114152.fctrNo                              0.0000000   0.0000000
## Q114152.fctrYes                             0.0000000   0.0000000
## Q114386.fctrMysterious                      0.0000000   0.0000000
## Q114517.fctrNo                              0.0000000   0.0000000
## Q114517.fctrYes                             0.0000000   0.0000000
## Q114748.fctrNo                              0.0000000   0.0000000
## Q114748.fctrYes                             0.0000000   0.0000000
## Q114961.fctrNo                              0.0000000   0.0000000
## Q114961.fctrYes                             0.0000000   0.0000000
## Q115195.fctrNo                              0.0000000   0.0000000
## Q115195.fctrYes                             0.0000000   0.0000000
## Q115390.fctrNo                              0.0000000   0.0000000
## Q115390.fctrYes                             0.0000000   0.0000000
## Q115602.fctrNo                              0.0000000   0.0000000
## Q115602.fctrYes                             0.0000000   0.0000000
## Q115610.fctrNo                              0.0000000   0.0000000
## Q115610.fctrYes                             0.0000000   0.0000000
## Q115777.fctrEnd                             0.0000000   0.0000000
## Q115777.fctrStart                           0.0000000   0.0000000
## Q115899.fctrMe                              0.0000000   0.0000000
## Q116197.fctrA.M.                            0.0000000   0.0000000
## Q116197.fctrP.M.                            0.0000000   0.0000000
## Q116448.fctrNo                              0.0000000   0.0000000
## Q116448.fctrYes                             0.0000000   0.0000000
## Q116601.fctrYes                             0.0000000   0.0000000
## Q116797.fctrNo                              0.0000000   0.0000000
## Q116797.fctrYes                             0.0000000   0.0000000
## Q116881.fctrHappy                           0.0000000   0.0000000
## Q116881.fctrRight                           0.0000000   0.0000000
## Q116953.fctrNo                              0.0000000   0.0000000
## Q116953.fctrYes                             0.0000000   0.0000000
## Q117186.fctrCool headed                     0.0000000   0.0000000
## Q117186.fctrHot headed                      0.0000000   0.0000000
## Q117193.fctrOdd hours                       0.0000000   0.0000000
## Q117193.fctrStandard hours                  0.0000000   0.0000000
## Q118117.fctrNo                              0.0000000   0.0000000
## Q118117.fctrYes                             0.0000000   0.0000000
## Q118232.fctrId                              0.0000000   0.0000000
## Q118232.fctrPr                              0.0000000   0.0000000
## Q118233.fctrNo                              0.0000000   0.0000000
## Q118233.fctrYes                             0.0000000   0.0000000
## Q118237.fctrNo                              0.0000000   0.0000000
## Q118237.fctrYes                             0.0000000   0.0000000
## Q118892.fctrNo                              0.0000000   0.0000000
## Q118892.fctrYes                             0.0000000   0.0000000
## Q119334.fctrNo                              0.0000000   0.0000000
## Q119334.fctrYes                             0.0000000   0.0000000
## Q119650.fctrGiving                          0.0000000   0.0000000
## Q119650.fctrReceiving                       0.0000000   0.0000000
## Q119851.fctrYes                             0.0000000   0.0000000
## Q120012.fctrYes                             0.0000000   0.0000000
## Q120014.fctrNo                              0.0000000   0.0000000
## Q120014.fctrYes                             0.0000000   0.0000000
## Q120194.fctrStudy first                     0.0000000   0.0000000
## Q120194.fctrTry first                       0.0000000   0.0000000
## Q120472.fctrArt                             0.0000000   0.0000000
## Q120650.fctrYes                             0.0000000   0.0000000
## Q120978.fctrNo                              0.0000000   0.0000000
## Q120978.fctrYes                             0.0000000   0.0000000
## Q121011.fctrNo                              0.0000000   0.0000000
## Q121011.fctrYes                             0.0000000   0.0000000
## Q121699.fctrNo                              0.0000000   0.0000000
## Q121699.fctrYes                             0.0000000   0.0000000
## Q121700.fctrNo                              0.0000000   0.0000000
## Q121700.fctrYes                             0.0000000   0.0000000
## Q122120.fctrNo                              0.0000000   0.0000000
## Q122120.fctrYes                             0.0000000   0.0000000
## Q122769.fctrNo                              0.0000000   0.0000000
## Q122769.fctrYes                             0.0000000   0.0000000
## Q122770.fctrNo                              0.0000000   0.0000000
## Q122770.fctrYes                             0.0000000   0.0000000
## Q122771.fctrPc                              0.0000000   0.0000000
## Q123464.fctrNo                              0.0000000   0.0000000
## Q123464.fctrYes                             0.0000000   0.0000000
## Q123621.fctrNo                              0.0000000   0.0000000
## Q123621.fctrYes                             0.0000000   0.0000000
## Q124122.fctrNo                              0.0000000   0.0000000
## Q124122.fctrYes                             0.0000000   0.0000000
## Q124742.fctrNo                              0.0000000   0.0000000
## Q124742.fctrYes                             0.0000000   0.0000000
## Q96024.fctrNo                               0.0000000   0.0000000
## Q96024.fctrYes                              0.0000000   0.0000000
## Q98059.fctrOnly-child                       0.0000000   0.0000000
## Q98059.fctrYes                              0.0000000   0.0000000
## Q98078.fctrNo                               0.0000000   0.0000000
## Q98078.fctrYes                              0.0000000   0.0000000
## Q98197.fctrYes                              0.0000000   0.0000000
## Q98578.fctrNo                               0.0000000   0.0000000
## Q98578.fctrYes                              0.0000000   0.0000000
## Q98869.fctrYes                              0.0000000   0.0000000
## Q99480.fctrNo                               0.0000000   0.0000000
## Q99581.fctrNo                               0.0000000   0.0000000
## Q99581.fctrYes                              0.0000000   0.0000000
## Q99716.fctrNo                               0.0000000   0.0000000
## Q99716.fctrYes                              0.0000000   0.0000000
## Q99982.fctrCheck!                           0.0000000   0.0000000
## Q99982.fctrNope                             0.0000000   0.0000000
## YOB.Age.fctr.L                              0.0000000   0.0000000
## YOB.Age.fctr.Q                              0.0000000   0.0000000
## YOB.Age.fctr.C                              0.0000000   0.0000000
## YOB.Age.fctr^4                              0.0000000   0.0000000
## YOB.Age.fctr^5                              0.0000000   0.0000000
## YOB.Age.fctr^6                              0.0000000   0.0000000
## YOB.Age.fctr^7                              0.0000000   0.0000000
## Hhold.fctrN:.clusterid.fctr2                0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr2              0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr2              0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr2              0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr2              0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr2              0.0000000   0.0000000
## Hhold.fctrSKy:.clusterid.fctr2              0.0000000   0.0000000
## Hhold.fctrN:.clusterid.fctr3                0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr3              0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr3              0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr3              0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr3              0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr3              0.0000000   0.0000000
## Hhold.fctrSKy:.clusterid.fctr3              0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr4              0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr4              0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr4              0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr4              0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr4              0.0000000   0.0000000
## Hhold.fctrSKy:.clusterid.fctr4              0.0000000   0.0000000
## Hhold.fctrN:.clusterid.fctr5                0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr5              0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr5              0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr5              0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr5              0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr5              0.0000000   0.0000000
## Hhold.fctrSKy:.clusterid.fctr5              0.0000000   0.0000000
## YOB.Age.fctrNA:YOB.Age.dff                  0.0000000   0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff             0.0000000   0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff             0.0000000   0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff             0.0000000   0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff             0.0000000   0.0000000
## YOB.Age.fctr(35,40]:YOB.Age.dff             0.0000000   0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff             0.0000000   0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff             0.0000000   0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff             0.0000000   0.0000000
## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 108

## Loading required package: lazyeval

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    1264          R                         0.2112086
## 2    2598          R                         0.2612503
## 3    1792          R                         0.2946746
## 4     279          R                         0.3072918
## 5    5135          R                         0.3160730
## 6     679          R                         0.3374351
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            D                             TRUE
## 2                            D                             TRUE
## 3                            D                             TRUE
## 4                            D                             TRUE
## 5                            D                             TRUE
## 6                            D                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.7887914                               FALSE
## 2                            0.7387497                               FALSE
## 3                            0.7053254                               FALSE
## 4                            0.6927082                               FALSE
## 5                            0.6839270                               FALSE
## 6                            0.6625649                               FALSE
##   Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1                                 FALSE                         -0.2387914
## 2                                 FALSE                         -0.1887497
## 3                                 FALSE                         -0.1553254
## 4                                 FALSE                         -0.1427082
## 5                                 FALSE                         -0.1339270
## 6                                 FALSE                         -0.1125649
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 35     4284          R                         0.4237533
## 41     6951          R                         0.4354580
## 63     6767          D                         0.4550297
## 84     6668          D                         0.4717538
## 99     5039          D                         0.4848338
## 100    5886          D                         0.4854435
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 35                             D                             TRUE
## 41                             D                             TRUE
## 63                             R                             TRUE
## 84                             R                             TRUE
## 99                             R                             TRUE
## 100                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 35                             0.5762467
## 41                             0.5645420
## 63                             0.4550297
## 84                             0.4717538
## 99                             0.4848338
## 100                            0.4854435
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 35                                FALSE
## 41                                FALSE
## 63                                FALSE
## 84                                FALSE
## 99                                FALSE
## 100                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 35                                  FALSE
## 41                                  FALSE
## 63                                  FALSE
## 84                                  FALSE
## 99                                  FALSE
## 100                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 35                        -0.026246690
## 41                        -0.014542032
## 63                         0.005029689
## 84                         0.021753786
## 99                         0.034833788
## 100                        0.035443494
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 173     521          D                         0.6466044
## 174     126          D                         0.6639871
## 175    1365          D                         0.6686585
## 176    1760          D                         0.6743803
## 177    5764          D                         0.6818120
## 178    4361          D                         0.6854357
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 173                            R                             TRUE
## 174                            R                             TRUE
## 175                            R                             TRUE
## 176                            R                             TRUE
## 177                            R                             TRUE
## 178                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 173                            0.6466044
## 174                            0.6639871
## 175                            0.6686585
## 176                            0.6743803
## 177                            0.6818120
## 178                            0.6854357
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 173                               FALSE
## 174                               FALSE
## 175                               FALSE
## 176                               FALSE
## 177                               FALSE
## 178                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 173                                 FALSE
## 174                                 FALSE
## 175                                 FALSE
## 176                                 FALSE
## 177                                 FALSE
## 178                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 173                          0.1966044
## 174                          0.2139871
## 175                          0.2186585
## 176                          0.2243803
## 177                          0.2318120
## 178                          0.2354357

##     Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKy        PKy      2     28      2     0.01608271    0.004514673
## N            N     48    209     59     0.12004595    0.108352144
## MKn        MKn     40    188     49     0.10798392    0.090293454
## SKn        SKn    222    781    276     0.44859276    0.501128668
## PKn        PKn     11     56     12     0.03216542    0.024830700
## SKy        SKy     23     47     28     0.02699598    0.051918736
## MKy        MKy     97    432    121     0.24813326    0.218961625
##     .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKy    0.003656307        13.96326        0.4986880     28        1.064814
## N      0.107861060        99.87826        0.4778864    209       24.374964
## MKn    0.089579525        89.83789        0.4778611    188       19.844175
## SKn    0.504570384       369.05678        0.4725439    781      107.440151
## PKn    0.021937843        24.99773        0.4463880     56        5.146900
## SKy    0.051188300        22.63033        0.4814964     47       10.753683
## MKy    0.221206581       205.43862        0.4755524    432       45.180345
##     err.abs.OOB.mean
## PKy        0.5324071
## N          0.5078118
## MKn        0.4961044
## SKn        0.4839646
## PKn        0.4679000
## SKy        0.4675514
## MKy        0.4657767
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##       443.000000      1741.000000       547.000000         1.000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##         1.000000         1.000000       825.802871         3.330416 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##      1741.000000       213.805031         3.421516
##              label step_major step_minor label_minor    bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 14.874  NA      NA
##        label step_major step_minor label_minor    bgn    end elapsed
## 2 fit.models          1          1           1  9.421 14.882   5.461
## 3 fit.models          1          2           2 14.883     NA      NA
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)
##               label step_major step_minor label_minor    bgn    end
## 3        fit.models          1          2           2 14.883 17.275
## 4 fit.data.training          2          0           0 17.276     NA
##   elapsed
## 3   2.393
## 4      NA

Step 2.0: fit data training

```{r fit.data.training_0, cache=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Final.All.X###glmnet"
## [1] "    indepVar: Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.668000 secs"
## Fitting alpha = 0.775, lambda = 0.025 on full training set
## [1] "myfit_mdl: train complete: 2.562000 secs"
##   alpha     lambda
## 1 0.775 0.02496316
##             Length Class      Mode     
## a0             68  -none-     numeric  
## beta        18156  dgCMatrix  S4       
## df             68  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         68  -none-     numeric  
## dev.ratio      68  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        267  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##                     (Intercept)                      Edn.fctr.L 
##                   -0.2449225766                   -0.0410550640 
##                      Edn.fctr^4                      Edn.fctr^7 
##                    0.0454409419                   -0.0145851497 
##                    Gender.fctrM                   Hhold.fctrPKn 
##                    0.1966959279                   -0.0189078124 
##                  Q100562.fctrNo                 Q101163.fctrMom 
##                    0.0628470650                   -0.3328493551 
##                 Q101596.fctrYes                 Q102674.fctrYes 
##                    0.0043428328                   -0.1118121020 
##                  Q106389.fctrNo       Q108950.fctrRisk-friendly 
##                    0.1003703013                   -0.0054863442 
##                  Q113181.fctrNo                 Q113181.fctrYes 
##                   -0.0506639944                    0.2526399278 
##               Q113583.fctrTunes                 Q114386.fctrTMI 
##                   -0.1010925969                   -0.0037320842 
##                  Q115195.fctrNo                  Q115390.fctrNo 
##                    0.0453270155                    0.0086776011 
##                  Q115611.fctrNo                 Q115611.fctrYes 
##                   -0.1769624318                    0.2753944470 
##                  Q116441.fctrNo                 Q116441.fctrYes 
##                   -0.0419307307                    0.0431055648 
##                  Q116601.fctrNo                  Q119851.fctrNo 
##                    0.2794407617                    0.1193109156 
##                 Q119851.fctrYes                  Q120379.fctrNo 
##                   -0.0149132779                    0.0173888231 
##                 Q120379.fctrYes                  Q120650.fctrNo 
##                   -0.0006833867                   -0.1124462251 
##                   Q98197.fctrNo                  Q98197.fctrYes 
##                   -0.2911579078                    0.0417988341 
##                   Q98869.fctrNo                  Q99480.fctrYes 
##                   -0.1005502983                    0.0474561517 
##    Hhold.fctrN:.clusterid.fctr4 YOB.Age.fctr(35,40]:YOB.Age.dff 
##                    0.0845740061                   -0.0095068786 
## [1] "max lambda < lambdaOpt:"
##                     (Intercept)                      Edn.fctr.L 
##                    -0.245950297                    -0.057195675 
##                      Edn.fctr^4                      Edn.fctr^7 
##                     0.057500961                    -0.028350568 
##                    Gender.fctrM                   Hhold.fctrPKn 
##                     0.208238350                    -0.048146841 
##                   Income.fctr.C                  Q100562.fctrNo 
##                     0.022116444                     0.086882484 
##                 Q101163.fctrMom                 Q101596.fctrYes 
##                    -0.363993068                     0.027278446 
##                 Q102674.fctrYes                  Q106389.fctrNo 
##                    -0.148665951                     0.122532934 
##       Q108950.fctrRisk-friendly                  Q113181.fctrNo 
##                    -0.047278846                    -0.052619681 
##                 Q113181.fctrYes               Q113583.fctrTunes 
##                     0.270091792                    -0.122567439 
##                 Q114386.fctrTMI                  Q115195.fctrNo 
##                    -0.019174193                     0.066262276 
##                  Q115390.fctrNo                  Q115611.fctrNo 
##                     0.041674388                    -0.188607998 
##                 Q115611.fctrYes                  Q116441.fctrNo 
##                     0.276563672                    -0.063382320 
##                 Q116441.fctrYes                  Q116601.fctrNo 
##                     0.054162432                     0.312361684 
##                  Q119851.fctrNo                 Q119851.fctrYes 
##                     0.129217335                    -0.025837744 
##                  Q120379.fctrNo                 Q120379.fctrYes 
##                     0.023580773                    -0.009681821 
##                  Q120650.fctrNo                   Q98197.fctrNo 
##                    -0.141138151                    -0.311369790 
##                  Q98197.fctrYes                   Q98869.fctrNo 
##                     0.049100822                    -0.123528410 
##                  Q99480.fctrYes  Hhold.fctrSKy:.clusterid.fctr3 
##                     0.072087582                     0.051330545 
##    Hhold.fctrN:.clusterid.fctr4  Hhold.fctrSKy:.clusterid.fctr4 
##                     0.137360070                    -0.026007797 
## YOB.Age.fctr(35,40]:YOB.Age.dff 
##                    -0.017736905 
## [1] "myfit_mdl: train diagnostics complete: 2.655000 secs"
## Loading required namespace: pROC
## Loading required package: ROCR
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess

## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk

##          Prediction
## Reference   D   R
##         D 608 563
##         R 297 716
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.062271e-01   2.220914e-01   5.853758e-01   6.267929e-01   5.361722e-01 
## AccuracyPValue  McnemarPValue 
##   2.414562e-11   1.618794e-19 
## [1] "myfit_mdl: predict complete: 9.939000 secs"
##                     id
## 1 Final.All.X###glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           feats
## 1 Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               0                      1.814                 1.177
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5810463    0.8215201    0.3405726       0.6591863
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.45       0.6247818        0.6062271
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5853758             0.6267929     0.2220914
## [1] "myfit_mdl: exit: 9.960000 secs"
##               label step_major step_minor label_minor    bgn    end
## 4 fit.data.training          2          0           0 17.276 27.741
## 5 fit.data.training          2          1           1 27.742     NA
##   elapsed
## 4  10.466
## 5      NA
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glbMdlFinId)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glbMdlFinId)$feats, ","))
    if (glb_is_classification)
    #     mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    # mdlEnsembleComps <- gsub(paste0("^", 
    #                     gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
    #                          "", mdlEnsembleComps)
        mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
                        mygetPredictIds(glb_rsp_var, thsMdlId)$prob  %in% mdlEnsembleComps)] else
        mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
                        mygetPredictIds(glb_rsp_var, thsMdlId)$value  %in% mdlEnsembleComps)]
                        
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        # glb_fin_mdl uses the same coefficients as glb_sel_mdl, 
        #   so copy the "Final" columns into "non-Final" columns
        glbObsTrn[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
            glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
        glbObsNew[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
            glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)
## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.45
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
##                                 All.X..rcv.glmnet.imp
## Q101163.fctrMom                            88.9098425
## Q98197.fctrNo                              41.3739497
## Q116601.fctrNo                             14.6114962
## Q115611.fctrYes                            82.6333748
## Q113181.fctrYes                           100.0000000
## Gender.fctrM                               45.6503194
## Q115611.fctrNo                             37.0447808
## Q119851.fctrNo                             29.5349743
## Q102674.fctrYes                            26.3084312
## Q120650.fctrNo                             33.9820515
## Q113583.fctrTunes                           8.0266221
## Q98869.fctrNo                              23.4958757
## Q106389.fctrNo                             20.7891725
## Hhold.fctrN:.clusterid.fctr4               39.7168373
## Q100562.fctrNo                             15.7468265
## Q113181.fctrNo                              0.0000000
## Q99480.fctrYes                              3.2766182
## Q115195.fctrNo                              0.0000000
## Edn.fctr^4                                 19.8530900
## Q116441.fctrYes                            23.9332042
## Q116441.fctrNo                             18.1901772
## Edn.fctr.L                                  6.3143991
## Q98197.fctrYes                              0.0000000
## Hhold.fctrPKn                              33.2892285
## Q120379.fctrNo                             12.2533356
## Q119851.fctrYes                             0.0000000
## Edn.fctr^7                                  1.8595355
## Q115390.fctrNo                              0.0000000
## YOB.Age.fctr(35,40]:YOB.Age.dff             0.0000000
## Q108950.fctrRisk-friendly                  27.4476100
## Q101596.fctrYes                             0.4989205
## Hhold.fctrSKy:.clusterid.fctr3              0.0000000
## Q114386.fctrTMI                            26.0760004
## Hhold.fctrSKy:.clusterid.fctr4              0.0000000
## Income.fctr.C                               0.0000000
## Q120379.fctrYes                             6.9206106
## .rnorm                                      0.0000000
## Edn.fctr.C                                  0.0000000
## Edn.fctr.Q                                  0.0000000
## Edn.fctr^5                                  0.0000000
## Edn.fctr^6                                  0.0000000
## Gender.fctrF                                0.0000000
## Hhold.fctrMKn                               0.0000000
## Hhold.fctrMKn:.clusterid.fctr2              0.0000000
## Hhold.fctrMKn:.clusterid.fctr3              0.0000000
## Hhold.fctrMKn:.clusterid.fctr4              0.0000000
## Hhold.fctrMKn:.clusterid.fctr5              0.0000000
## Hhold.fctrMKy                               0.0000000
## Hhold.fctrMKy:.clusterid.fctr2              0.0000000
## Hhold.fctrMKy:.clusterid.fctr3              0.0000000
## Hhold.fctrMKy:.clusterid.fctr4              0.0000000
## Hhold.fctrMKy:.clusterid.fctr5              0.0000000
## Hhold.fctrN:.clusterid.fctr2                0.0000000
## Hhold.fctrN:.clusterid.fctr3                0.0000000
## Hhold.fctrN:.clusterid.fctr5                0.0000000
## Hhold.fctrPKn:.clusterid.fctr2              0.0000000
## Hhold.fctrPKn:.clusterid.fctr3              0.0000000
## Hhold.fctrPKn:.clusterid.fctr4              0.0000000
## Hhold.fctrPKn:.clusterid.fctr5              0.0000000
## Hhold.fctrPKy                               8.9203943
## Hhold.fctrPKy:.clusterid.fctr2              0.0000000
## Hhold.fctrPKy:.clusterid.fctr3              0.0000000
## Hhold.fctrPKy:.clusterid.fctr4              0.0000000
## Hhold.fctrPKy:.clusterid.fctr5              0.0000000
## Hhold.fctrSKn                               0.0000000
## Hhold.fctrSKn:.clusterid.fctr2              0.0000000
## Hhold.fctrSKn:.clusterid.fctr3              0.0000000
## Hhold.fctrSKn:.clusterid.fctr4              0.0000000
## Hhold.fctrSKn:.clusterid.fctr5              0.0000000
## Hhold.fctrSKy                               0.0000000
## Hhold.fctrSKy:.clusterid.fctr2              0.0000000
## Hhold.fctrSKy:.clusterid.fctr5              0.0000000
## Income.fctr.L                               0.0000000
## Income.fctr.Q                               0.0000000
## Income.fctr^4                               0.0000000
## Income.fctr^5                               0.0000000
## Income.fctr^6                               0.0000000
## Q100010.fctrNo                              0.0000000
## Q100010.fctrYes                             0.0000000
## Q100562.fctrYes                             0.0000000
## Q100680.fctrNo                              0.0000000
## Q100680.fctrYes                             0.0000000
## Q100689.fctrNo                              0.0000000
## Q100689.fctrYes                             0.0000000
## Q101162.fctrOptimist                        0.0000000
## Q101162.fctrPessimist                       0.0000000
## Q101163.fctrDad                             0.0000000
## Q101596.fctrNo                              0.0000000
## Q102089.fctrOwn                             0.0000000
## Q102089.fctrRent                            0.0000000
## Q102289.fctrNo                              0.0000000
## Q102289.fctrYes                             0.0000000
## Q102674.fctrNo                              0.0000000
## Q102687.fctrNo                              0.0000000
## Q102687.fctrYes                             0.0000000
## Q102906.fctrNo                              0.0000000
## Q102906.fctrYes                             0.0000000
## Q103293.fctrNo                              0.0000000
## Q103293.fctrYes                             0.0000000
## Q104996.fctrNo                              0.0000000
## Q104996.fctrYes                             0.0000000
## Q105655.fctrNo                              0.0000000
## Q105655.fctrYes                             0.0000000
## Q105840.fctrNo                              0.0000000
## Q105840.fctrYes                             0.0000000
## Q106042.fctrNo                              0.0000000
## Q106042.fctrYes                             0.0000000
## Q106272.fctrNo                              0.0000000
## Q106272.fctrYes                             0.0000000
## Q106388.fctrNo                              0.0000000
## Q106388.fctrYes                             0.0000000
## Q106389.fctrYes                             0.0000000
## Q106993.fctrNo                              0.0000000
## Q106993.fctrYes                             0.0000000
## Q106997.fctrGr                              0.0000000
## Q106997.fctrYy                              0.0000000
## Q107491.fctrNo                              0.0000000
## Q107491.fctrYes                             0.0000000
## Q107869.fctrNo                              0.0000000
## Q107869.fctrYes                             0.0000000
## Q108342.fctrIn-person                       0.0000000
## Q108342.fctrOnline                          0.0000000
## Q108343.fctrNo                              0.0000000
## Q108343.fctrYes                             0.0000000
## Q108617.fctrNo                              0.0000000
## Q108617.fctrYes                             0.0000000
## Q108754.fctrNo                              0.0000000
## Q108754.fctrYes                             0.0000000
## Q108855.fctrUmm...                          0.0000000
## Q108855.fctrYes!                            0.0000000
## Q108856.fctrSocialize                       0.0000000
## Q108856.fctrSpace                           0.0000000
## Q108950.fctrCautious                        0.0000000
## Q109367.fctrNo                              0.0000000
## Q109367.fctrYes                            18.0382944
## Q110740.fctrMac                             0.0000000
## Q110740.fctrPC                              0.0000000
## Q111220.fctrNo                              0.0000000
## Q111220.fctrYes                             0.0000000
## Q111580.fctrDemanding                       0.0000000
## Q111580.fctrSupportive                      0.0000000
## Q111848.fctrNo                              0.0000000
## Q111848.fctrYes                            18.3541103
## Q112270.fctrNo                              0.0000000
## Q112270.fctrYes                             0.0000000
## Q112478.fctrNo                              0.0000000
## Q112478.fctrYes                             0.0000000
## Q112512.fctrNo                              0.0000000
## Q112512.fctrYes                             0.0000000
## Q113583.fctrTalk                            0.0000000
## Q113584.fctrPeople                          0.0000000
## Q113584.fctrTechnology                      0.0000000
## Q113992.fctrNo                              0.0000000
## Q113992.fctrYes                             0.0000000
## Q114152.fctrNo                              0.0000000
## Q114152.fctrYes                             0.0000000
## Q114386.fctrMysterious                      0.0000000
## Q114517.fctrNo                              0.0000000
## Q114517.fctrYes                             0.0000000
## Q114748.fctrNo                              0.0000000
## Q114748.fctrYes                             0.0000000
## Q114961.fctrNo                              0.0000000
## Q114961.fctrYes                             0.0000000
## Q115195.fctrYes                             0.0000000
## Q115390.fctrYes                             0.0000000
## Q115602.fctrNo                              0.0000000
## Q115602.fctrYes                             0.0000000
## Q115610.fctrNo                              0.0000000
## Q115610.fctrYes                             0.0000000
## Q115777.fctrEnd                             0.0000000
## Q115777.fctrStart                           0.0000000
## Q115899.fctrCs                              2.9965936
## Q115899.fctrMe                              0.0000000
## Q116197.fctrA.M.                            0.0000000
## Q116197.fctrP.M.                            0.0000000
## Q116448.fctrNo                              0.0000000
## Q116448.fctrYes                             0.0000000
## Q116601.fctrYes                             0.0000000
## Q116797.fctrNo                              0.0000000
## Q116797.fctrYes                             0.0000000
## Q116881.fctrHappy                           0.0000000
## Q116881.fctrRight                           0.0000000
## Q116953.fctrNo                              0.0000000
## Q116953.fctrYes                             0.0000000
## Q117186.fctrCool headed                     0.0000000
## Q117186.fctrHot headed                      0.0000000
## Q117193.fctrOdd hours                       0.0000000
## Q117193.fctrStandard hours                  0.0000000
## Q118117.fctrNo                              0.0000000
## Q118117.fctrYes                             0.0000000
## Q118232.fctrId                              0.0000000
## Q118232.fctrPr                              0.0000000
## Q118233.fctrNo                              0.0000000
## Q118233.fctrYes                             0.0000000
## Q118237.fctrNo                              0.0000000
## Q118237.fctrYes                             0.0000000
## Q118892.fctrNo                              0.0000000
## Q118892.fctrYes                             0.0000000
## Q119334.fctrNo                              0.0000000
## Q119334.fctrYes                             0.0000000
## Q119650.fctrGiving                          0.0000000
## Q119650.fctrReceiving                       0.0000000
## Q120012.fctrNo                              1.9105692
## Q120012.fctrYes                             0.0000000
## Q120014.fctrNo                              0.0000000
## Q120014.fctrYes                             0.0000000
## Q120194.fctrStudy first                     0.0000000
## Q120194.fctrTry first                       0.0000000
## Q120472.fctrArt                             0.0000000
## Q120472.fctrScience                         0.7386151
## Q120650.fctrYes                             0.0000000
## Q120978.fctrNo                              0.0000000
## Q120978.fctrYes                             0.0000000
## Q121011.fctrNo                              0.0000000
## Q121011.fctrYes                             0.0000000
## Q121699.fctrNo                              0.0000000
## Q121699.fctrYes                             0.0000000
## Q121700.fctrNo                              0.0000000
## Q121700.fctrYes                             0.0000000
## Q122120.fctrNo                              0.0000000
## Q122120.fctrYes                             0.0000000
## Q122769.fctrNo                              0.0000000
## Q122769.fctrYes                             0.0000000
## Q122770.fctrNo                              0.0000000
## Q122770.fctrYes                             0.0000000
## Q122771.fctrPc                              0.0000000
## Q122771.fctrPt                              3.4734080
## Q123464.fctrNo                              0.0000000
## Q123464.fctrYes                             0.0000000
## Q123621.fctrNo                              0.0000000
## Q123621.fctrYes                             0.0000000
## Q124122.fctrNo                              0.0000000
## Q124122.fctrYes                             0.0000000
## Q124742.fctrNo                              0.0000000
## Q124742.fctrYes                             0.0000000
## Q96024.fctrNo                               0.0000000
## Q96024.fctrYes                              0.0000000
## Q98059.fctrOnly-child                       0.0000000
## Q98059.fctrYes                              0.0000000
## Q98078.fctrNo                               0.0000000
## Q98078.fctrYes                              0.0000000
## Q98578.fctrNo                               0.0000000
## Q98578.fctrYes                              0.0000000
## Q98869.fctrYes                              0.0000000
## Q99480.fctrNo                               0.0000000
## Q99581.fctrNo                               0.0000000
## Q99581.fctrYes                              0.0000000
## Q99716.fctrNo                               0.0000000
## Q99716.fctrYes                              0.0000000
## Q99982.fctrCheck!                           0.0000000
## Q99982.fctrNope                             0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff             0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff             0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff             0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff             0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff             0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff             0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff             0.0000000
## YOB.Age.fctr.C                              0.0000000
## YOB.Age.fctr.L                              0.0000000
## YOB.Age.fctr.Q                              0.0000000
## YOB.Age.fctrNA:YOB.Age.dff                  0.0000000
## YOB.Age.fctr^4                              0.0000000
## YOB.Age.fctr^5                              0.0000000
## YOB.Age.fctr^6                              0.0000000
## YOB.Age.fctr^7                              0.0000000
## YOB.Age.fctr^8                             17.5760073
##                                 Final.All.X...glmnet.imp         imp
## Q101163.fctrMom                              100.0000000 100.0000000
## Q98197.fctrNo                                 87.2531689  87.2531689
## Q116601.fctrNo                                84.1672679  84.1672679
## Q115611.fctrYes                               81.9645272  81.9645272
## Q113181.fctrYes                               75.7075272  75.7075272
## Gender.fctrM                                  58.8786885  58.8786885
## Q115611.fctrNo                                53.0113697  53.0113697
## Q119851.fctrNo                                35.8057666  35.8057666
## Q102674.fctrYes                               34.4227650  34.4227650
## Q120650.fctrNo                                34.3546159  34.3546159
## Q113583.fctrTunes                             30.7499297  30.7499297
## Q98869.fctrNo                                 30.6358969  30.6358969
## Q106389.fctrNo                                30.5566920  30.5566920
## Hhold.fctrN:.clusterid.fctr4                  26.8209099  26.8209099
## Q100562.fctrNo                                19.4527392  19.4527392
## Q113181.fctrNo                                15.1336817  15.1336817
## Q99480.fctrYes                                14.8928124  14.8928124
## Q115195.fctrNo                                14.1431194  14.1431194
## Edn.fctr^4                                    13.8977728  13.8977728
## Q116441.fctrYes                               13.1714542  13.1714542
## Q116441.fctrNo                                13.1489957  13.1489957
## Edn.fctr.L                                    12.7213940  12.7213940
## Q98197.fctrYes                                12.6645733  12.6645733
## Hhold.fctrPKn                                  6.5448673   6.5448673
## Q120379.fctrNo                                 5.3678570   5.3678570
## Q119851.fctrYes                                4.7802989   4.7802989
## Edn.fctr^7                                     4.7720672   4.7720672
## Q115390.fctrNo                                 3.6196868   3.6196868
## YOB.Age.fctr(35,40]:YOB.Age.dff                3.0871622   3.0871622
## Q108950.fctrRisk-friendly                      2.9470490   2.9470490
## Q101596.fctrYes                                2.0135751   2.0135751
## Hhold.fctrSKy:.clusterid.fctr3                 1.6149959   1.6149959
## Q114386.fctrTMI                                1.5961164   1.5961164
## Hhold.fctrSKy:.clusterid.fctr4                 0.8182747   0.8182747
## Income.fctr.C                                  0.6958423   0.6958423
## Q120379.fctrYes                                0.4864171   0.4864171
## .rnorm                                         0.0000000   0.0000000
## Edn.fctr.C                                     0.0000000   0.0000000
## Edn.fctr.Q                                     0.0000000   0.0000000
## Edn.fctr^5                                     0.0000000   0.0000000
## Edn.fctr^6                                     0.0000000   0.0000000
## Gender.fctrF                                   0.0000000   0.0000000
## Hhold.fctrMKn                                  0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr2                 0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr3                 0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr4                 0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr5                 0.0000000   0.0000000
## Hhold.fctrMKy                                  0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr2                 0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr3                 0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr4                 0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr5                 0.0000000   0.0000000
## Hhold.fctrN:.clusterid.fctr2                   0.0000000   0.0000000
## Hhold.fctrN:.clusterid.fctr3                   0.0000000   0.0000000
## Hhold.fctrN:.clusterid.fctr5                   0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr2                 0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr3                 0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr4                 0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr5                 0.0000000   0.0000000
## Hhold.fctrPKy                                  0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr2                 0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr3                 0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr4                 0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr5                 0.0000000   0.0000000
## Hhold.fctrSKn                                  0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr2                 0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr3                 0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr4                 0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr5                 0.0000000   0.0000000
## Hhold.fctrSKy                                  0.0000000   0.0000000
## Hhold.fctrSKy:.clusterid.fctr2                 0.0000000   0.0000000
## Hhold.fctrSKy:.clusterid.fctr5                 0.0000000   0.0000000
## Income.fctr.L                                  0.0000000   0.0000000
## Income.fctr.Q                                  0.0000000   0.0000000
## Income.fctr^4                                  0.0000000   0.0000000
## Income.fctr^5                                  0.0000000   0.0000000
## Income.fctr^6                                  0.0000000   0.0000000
## Q100010.fctrNo                                 0.0000000   0.0000000
## Q100010.fctrYes                                0.0000000   0.0000000
## Q100562.fctrYes                                0.0000000   0.0000000
## Q100680.fctrNo                                 0.0000000   0.0000000
## Q100680.fctrYes                                0.0000000   0.0000000
## Q100689.fctrNo                                 0.0000000   0.0000000
## Q100689.fctrYes                                0.0000000   0.0000000
## Q101162.fctrOptimist                           0.0000000   0.0000000
## Q101162.fctrPessimist                          0.0000000   0.0000000
## Q101163.fctrDad                                0.0000000   0.0000000
## Q101596.fctrNo                                 0.0000000   0.0000000
## Q102089.fctrOwn                                0.0000000   0.0000000
## Q102089.fctrRent                               0.0000000   0.0000000
## Q102289.fctrNo                                 0.0000000   0.0000000
## Q102289.fctrYes                                0.0000000   0.0000000
## Q102674.fctrNo                                 0.0000000   0.0000000
## Q102687.fctrNo                                 0.0000000   0.0000000
## Q102687.fctrYes                                0.0000000   0.0000000
## Q102906.fctrNo                                 0.0000000   0.0000000
## Q102906.fctrYes                                0.0000000   0.0000000
## Q103293.fctrNo                                 0.0000000   0.0000000
## Q103293.fctrYes                                0.0000000   0.0000000
## Q104996.fctrNo                                 0.0000000   0.0000000
## Q104996.fctrYes                                0.0000000   0.0000000
## Q105655.fctrNo                                 0.0000000   0.0000000
## Q105655.fctrYes                                0.0000000   0.0000000
## Q105840.fctrNo                                 0.0000000   0.0000000
## Q105840.fctrYes                                0.0000000   0.0000000
## Q106042.fctrNo                                 0.0000000   0.0000000
## Q106042.fctrYes                                0.0000000   0.0000000
## Q106272.fctrNo                                 0.0000000   0.0000000
## Q106272.fctrYes                                0.0000000   0.0000000
## Q106388.fctrNo                                 0.0000000   0.0000000
## Q106388.fctrYes                                0.0000000   0.0000000
## Q106389.fctrYes                                0.0000000   0.0000000
## Q106993.fctrNo                                 0.0000000   0.0000000
## Q106993.fctrYes                                0.0000000   0.0000000
## Q106997.fctrGr                                 0.0000000   0.0000000
## Q106997.fctrYy                                 0.0000000   0.0000000
## Q107491.fctrNo                                 0.0000000   0.0000000
## Q107491.fctrYes                                0.0000000   0.0000000
## Q107869.fctrNo                                 0.0000000   0.0000000
## Q107869.fctrYes                                0.0000000   0.0000000
## Q108342.fctrIn-person                          0.0000000   0.0000000
## Q108342.fctrOnline                             0.0000000   0.0000000
## Q108343.fctrNo                                 0.0000000   0.0000000
## Q108343.fctrYes                                0.0000000   0.0000000
## Q108617.fctrNo                                 0.0000000   0.0000000
## Q108617.fctrYes                                0.0000000   0.0000000
## Q108754.fctrNo                                 0.0000000   0.0000000
## Q108754.fctrYes                                0.0000000   0.0000000
## Q108855.fctrUmm...                             0.0000000   0.0000000
## Q108855.fctrYes!                               0.0000000   0.0000000
## Q108856.fctrSocialize                          0.0000000   0.0000000
## Q108856.fctrSpace                              0.0000000   0.0000000
## Q108950.fctrCautious                           0.0000000   0.0000000
## Q109367.fctrNo                                 0.0000000   0.0000000
## Q109367.fctrYes                                0.0000000   0.0000000
## Q110740.fctrMac                                0.0000000   0.0000000
## Q110740.fctrPC                                 0.0000000   0.0000000
## Q111220.fctrNo                                 0.0000000   0.0000000
## Q111220.fctrYes                                0.0000000   0.0000000
## Q111580.fctrDemanding                          0.0000000   0.0000000
## Q111580.fctrSupportive                         0.0000000   0.0000000
## Q111848.fctrNo                                 0.0000000   0.0000000
## Q111848.fctrYes                                0.0000000   0.0000000
## Q112270.fctrNo                                 0.0000000   0.0000000
## Q112270.fctrYes                                0.0000000   0.0000000
## Q112478.fctrNo                                 0.0000000   0.0000000
## Q112478.fctrYes                                0.0000000   0.0000000
## Q112512.fctrNo                                 0.0000000   0.0000000
## Q112512.fctrYes                                0.0000000   0.0000000
## Q113583.fctrTalk                               0.0000000   0.0000000
## Q113584.fctrPeople                             0.0000000   0.0000000
## Q113584.fctrTechnology                         0.0000000   0.0000000
## Q113992.fctrNo                                 0.0000000   0.0000000
## Q113992.fctrYes                                0.0000000   0.0000000
## Q114152.fctrNo                                 0.0000000   0.0000000
## Q114152.fctrYes                                0.0000000   0.0000000
## Q114386.fctrMysterious                         0.0000000   0.0000000
## Q114517.fctrNo                                 0.0000000   0.0000000
## Q114517.fctrYes                                0.0000000   0.0000000
## Q114748.fctrNo                                 0.0000000   0.0000000
## Q114748.fctrYes                                0.0000000   0.0000000
## Q114961.fctrNo                                 0.0000000   0.0000000
## Q114961.fctrYes                                0.0000000   0.0000000
## Q115195.fctrYes                                0.0000000   0.0000000
## Q115390.fctrYes                                0.0000000   0.0000000
## Q115602.fctrNo                                 0.0000000   0.0000000
## Q115602.fctrYes                                0.0000000   0.0000000
## Q115610.fctrNo                                 0.0000000   0.0000000
## Q115610.fctrYes                                0.0000000   0.0000000
## Q115777.fctrEnd                                0.0000000   0.0000000
## Q115777.fctrStart                              0.0000000   0.0000000
## Q115899.fctrCs                                 0.0000000   0.0000000
## Q115899.fctrMe                                 0.0000000   0.0000000
## Q116197.fctrA.M.                               0.0000000   0.0000000
## Q116197.fctrP.M.                               0.0000000   0.0000000
## Q116448.fctrNo                                 0.0000000   0.0000000
## Q116448.fctrYes                                0.0000000   0.0000000
## Q116601.fctrYes                                0.0000000   0.0000000
## Q116797.fctrNo                                 0.0000000   0.0000000
## Q116797.fctrYes                                0.0000000   0.0000000
## Q116881.fctrHappy                              0.0000000   0.0000000
## Q116881.fctrRight                              0.0000000   0.0000000
## Q116953.fctrNo                                 0.0000000   0.0000000
## Q116953.fctrYes                                0.0000000   0.0000000
## Q117186.fctrCool headed                        0.0000000   0.0000000
## Q117186.fctrHot headed                         0.0000000   0.0000000
## Q117193.fctrOdd hours                          0.0000000   0.0000000
## Q117193.fctrStandard hours                     0.0000000   0.0000000
## Q118117.fctrNo                                 0.0000000   0.0000000
## Q118117.fctrYes                                0.0000000   0.0000000
## Q118232.fctrId                                 0.0000000   0.0000000
## Q118232.fctrPr                                 0.0000000   0.0000000
## Q118233.fctrNo                                 0.0000000   0.0000000
## Q118233.fctrYes                                0.0000000   0.0000000
## Q118237.fctrNo                                 0.0000000   0.0000000
## Q118237.fctrYes                                0.0000000   0.0000000
## Q118892.fctrNo                                 0.0000000   0.0000000
## Q118892.fctrYes                                0.0000000   0.0000000
## Q119334.fctrNo                                 0.0000000   0.0000000
## Q119334.fctrYes                                0.0000000   0.0000000
## Q119650.fctrGiving                             0.0000000   0.0000000
## Q119650.fctrReceiving                          0.0000000   0.0000000
## Q120012.fctrNo                                 0.0000000   0.0000000
## Q120012.fctrYes                                0.0000000   0.0000000
## Q120014.fctrNo                                 0.0000000   0.0000000
## Q120014.fctrYes                                0.0000000   0.0000000
## Q120194.fctrStudy first                        0.0000000   0.0000000
## Q120194.fctrTry first                          0.0000000   0.0000000
## Q120472.fctrArt                                0.0000000   0.0000000
## Q120472.fctrScience                            0.0000000   0.0000000
## Q120650.fctrYes                                0.0000000   0.0000000
## Q120978.fctrNo                                 0.0000000   0.0000000
## Q120978.fctrYes                                0.0000000   0.0000000
## Q121011.fctrNo                                 0.0000000   0.0000000
## Q121011.fctrYes                                0.0000000   0.0000000
## Q121699.fctrNo                                 0.0000000   0.0000000
## Q121699.fctrYes                                0.0000000   0.0000000
## Q121700.fctrNo                                 0.0000000   0.0000000
## Q121700.fctrYes                                0.0000000   0.0000000
## Q122120.fctrNo                                 0.0000000   0.0000000
## Q122120.fctrYes                                0.0000000   0.0000000
## Q122769.fctrNo                                 0.0000000   0.0000000
## Q122769.fctrYes                                0.0000000   0.0000000
## Q122770.fctrNo                                 0.0000000   0.0000000
## Q122770.fctrYes                                0.0000000   0.0000000
## Q122771.fctrPc                                 0.0000000   0.0000000
## Q122771.fctrPt                                 0.0000000   0.0000000
## Q123464.fctrNo                                 0.0000000   0.0000000
## Q123464.fctrYes                                0.0000000   0.0000000
## Q123621.fctrNo                                 0.0000000   0.0000000
## Q123621.fctrYes                                0.0000000   0.0000000
## Q124122.fctrNo                                 0.0000000   0.0000000
## Q124122.fctrYes                                0.0000000   0.0000000
## Q124742.fctrNo                                 0.0000000   0.0000000
## Q124742.fctrYes                                0.0000000   0.0000000
## Q96024.fctrNo                                  0.0000000   0.0000000
## Q96024.fctrYes                                 0.0000000   0.0000000
## Q98059.fctrOnly-child                          0.0000000   0.0000000
## Q98059.fctrYes                                 0.0000000   0.0000000
## Q98078.fctrNo                                  0.0000000   0.0000000
## Q98078.fctrYes                                 0.0000000   0.0000000
## Q98578.fctrNo                                  0.0000000   0.0000000
## Q98578.fctrYes                                 0.0000000   0.0000000
## Q98869.fctrYes                                 0.0000000   0.0000000
## Q99480.fctrNo                                  0.0000000   0.0000000
## Q99581.fctrNo                                  0.0000000   0.0000000
## Q99581.fctrYes                                 0.0000000   0.0000000
## Q99716.fctrNo                                  0.0000000   0.0000000
## Q99716.fctrYes                                 0.0000000   0.0000000
## Q99982.fctrCheck!                              0.0000000   0.0000000
## Q99982.fctrNope                                0.0000000   0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr.C                                 0.0000000   0.0000000
## YOB.Age.fctr.L                                 0.0000000   0.0000000
## YOB.Age.fctr.Q                                 0.0000000   0.0000000
## YOB.Age.fctrNA:YOB.Age.dff                     0.0000000   0.0000000
## YOB.Age.fctr^4                                 0.0000000   0.0000000
## YOB.Age.fctr^5                                 0.0000000   0.0000000
## YOB.Age.fctr^6                                 0.0000000   0.0000000
## YOB.Age.fctr^7                                 0.0000000   0.0000000
## YOB.Age.fctr^8                                 0.0000000   0.0000000
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId, 
            prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    1264          R                                NA
## 2    2598          R                                NA
## 3    1461          R                         0.2834618
## 4    5551          R                         0.3168096
## 5    3419          R                         0.2532728
## 6    3278          R                         0.3346711
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                         <NA>                               NA
## 2                         <NA>                               NA
## 3                            D                             TRUE
## 4                            D                             TRUE
## 5                            D                             TRUE
## 6                            D                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                                   NA                                  NA
## 2                                   NA                                  NA
## 3                            0.7165382                               FALSE
## 4                            0.6831904                               FALSE
## 5                            0.7467272                               FALSE
## 6                            0.6653289                               FALSE
##   Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 1                            0.2399740                               D
## 2                            0.2412746                               D
## 3                            0.2929028                               D
## 4                            0.2964347                               D
## 5                            0.2983740                               D
## 6                            0.3082703                               D
##   Party.fctr.Final.All.X...glmnet.err
## 1                                TRUE
## 2                                TRUE
## 3                                TRUE
## 4                                TRUE
## 5                                TRUE
## 6                                TRUE
##   Party.fctr.Final.All.X...glmnet.err.abs
## 1                               0.7600260
## 2                               0.7587254
## 3                               0.7070972
## 4                               0.7035653
## 5                               0.7016260
## 6                               0.6917297
##   Party.fctr.Final.All.X...glmnet.is.acc
## 1                                  FALSE
## 2                                  FALSE
## 3                                  FALSE
## 4                                  FALSE
## 5                                  FALSE
## 6                                  FALSE
##   Party.fctr.Final.All.X...glmnet.accurate
## 1                                    FALSE
## 2                                    FALSE
## 3                                    FALSE
## 4                                    FALSE
## 5                                    FALSE
## 6                                    FALSE
##   Party.fctr.Final.All.X...glmnet.error
## 1                            -0.2100260
## 2                            -0.2087254
## 3                            -0.1570972
## 4                            -0.1535653
## 5                            -0.1516260
## 6                            -0.1417297
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 83     2022          R                                NA
## 501    4000          D                         0.4717538
## 588    5681          D                         0.4473826
## 661    6282          D                         0.5047016
## 705    2318          D                         0.5428999
## 856    4350          D                         0.6431843
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 83                          <NA>                               NA
## 501                            R                             TRUE
## 588                            D                            FALSE
## 661                            R                             TRUE
## 705                            R                             TRUE
## 856                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 83                                    NA
## 501                            0.4717538
## 588                            0.4473826
## 661                            0.5047016
## 705                            0.5428999
## 856                            0.6431843
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 83                                   NA
## 501                               FALSE
## 588                                TRUE
## 661                               FALSE
## 705                               FALSE
## 856                               FALSE
##     Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 83                             0.3957951                               D
## 501                            0.4815699                               R
## 588                            0.4909613                               R
## 661                            0.5034475                               R
## 705                            0.5133360                               R
## 856                            0.6458690                               R
##     Party.fctr.Final.All.X...glmnet.err
## 83                                 TRUE
## 501                                TRUE
## 588                                TRUE
## 661                                TRUE
## 705                                TRUE
## 856                                TRUE
##     Party.fctr.Final.All.X...glmnet.err.abs
## 83                                0.6042049
## 501                               0.4815699
## 588                               0.4909613
## 661                               0.5034475
## 705                               0.5133360
## 856                               0.6458690
##     Party.fctr.Final.All.X...glmnet.is.acc
## 83                                   FALSE
## 501                                  FALSE
## 588                                  FALSE
## 661                                  FALSE
## 705                                  FALSE
## 856                                  FALSE
##     Party.fctr.Final.All.X...glmnet.accurate
## 83                                     FALSE
## 501                                    FALSE
## 588                                    FALSE
## 661                                    FALSE
## 705                                    FALSE
## 856                                    FALSE
##     Party.fctr.Final.All.X...glmnet.error
## 83                            -0.05420495
## 501                            0.03156994
## 588                            0.04096127
## 661                            0.05344749
## 705                            0.06333597
## 856                            0.19586903
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 855    3431          D                         0.6495350
## 856    4350          D                         0.6431843
## 857    1538          D                         0.6937111
## 858    4361          D                                NA
## 859     485          D                         0.6924804
## 860    2495          D                         0.7304513
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 855                            R                             TRUE
## 856                            R                             TRUE
## 857                            R                             TRUE
## 858                         <NA>                               NA
## 859                            R                             TRUE
## 860                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 855                            0.6495350
## 856                            0.6431843
## 857                            0.6937111
## 858                                   NA
## 859                            0.6924804
## 860                            0.7304513
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 855                               FALSE
## 856                               FALSE
## 857                               FALSE
## 858                                  NA
## 859                               FALSE
## 860                               FALSE
##     Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 855                            0.6355506                               R
## 856                            0.6458690                               R
## 857                            0.6513225                               R
## 858                            0.6556375                               R
## 859                            0.6691584                               R
## 860                            0.6862921                               R
##     Party.fctr.Final.All.X...glmnet.err
## 855                                TRUE
## 856                                TRUE
## 857                                TRUE
## 858                                TRUE
## 859                                TRUE
## 860                                TRUE
##     Party.fctr.Final.All.X...glmnet.err.abs
## 855                               0.6355506
## 856                               0.6458690
## 857                               0.6513225
## 858                               0.6556375
## 859                               0.6691584
## 860                               0.6862921
##     Party.fctr.Final.All.X...glmnet.is.acc
## 855                                  FALSE
## 856                                  FALSE
## 857                                  FALSE
## 858                                  FALSE
## 859                                  FALSE
## 860                                  FALSE
##     Party.fctr.Final.All.X...glmnet.accurate
## 855                                    FALSE
## 856                                    FALSE
## 857                                    FALSE
## 858                                    FALSE
## 859                                    FALSE
## 860                                    FALSE
##     Party.fctr.Final.All.X...glmnet.error
## 855                             0.1855506
## 856                             0.1958690
## 857                             0.2013225
## 858                             0.2056375
## 859                             0.2191584
## 860                             0.2362921

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))
## [1] "Party.fctr.Final.All.X...glmnet.prob"   
## [2] "Party.fctr.Final.All.X...glmnet"        
## [3] "Party.fctr.Final.All.X...glmnet.err"    
## [4] "Party.fctr.Final.All.X...glmnet.err.abs"
## [5] "Party.fctr.Final.All.X...glmnet.is.acc"
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))
## character(0)
print(setdiff(names(glbObsOOB), names(glbObsAll)))
## character(0)
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.training.all.prediction 
## 2.0000    5   2 0 0 1
## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)
## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  model.final 
## 3.0000    4   2 0 1 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)
##               label step_major step_minor label_minor    bgn    end
## 5 fit.data.training          2          1           1 27.742 34.252
## 6  predict.data.new          3          0           0 34.253     NA
##   elapsed
## 5   6.511
## 6      NA

Step 3.0: predict data new

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.45

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.45
## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108
## Warning: Removed 547 rows containing missing values (geom_point).

## Warning: Removed 547 rows containing missing values (geom_point).

## Warning: Removed 547 rows containing missing values (geom_point).

## Warning: Removed 547 rows containing missing values (geom_point).

## Warning: Removed 547 rows containing missing values (geom_point).

## Warning: Removed 547 rows containing missing values (geom_point).

## Warning: Removed 547 rows containing missing values (geom_point).

## Warning: Removed 547 rows containing missing values (geom_point).

## Warning: Removed 547 rows containing missing values (geom_point).

## Warning: Removed 547 rows containing missing values (geom_point).

## NULL
## Loading required package: tidyr
## 
## Attaching package: 'tidyr'
## The following object is masked from 'package:Matrix':
## 
##     expand
## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"
## [1] "Stacking file Q109244No_AllXpreProc_cnk03_rest_out_fin.csv to prediction outputs..."
## [1] 0.45
## [1] "glbMdlSelId: All.X##rcv#glmnet"
## [1] "glbMdlFinId: Final.All.X###glmnet"
## [1] "Cross Validation issues:"
##        MFO###myMFO_classfr  Random###myrandom_classfr 
##                          0                          0 
## Max.cor.Y.rcv.1X1###glmnet       Final.All.X###glmnet 
##                          0                          0
##                                 max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glmnet                      0.5981941       0.6177236
## Low.cor.X##rcv#glmnet                  0.5801354       0.6063250
## Interact.High.cor.Y##rcv#glmnet        0.5733634       0.5847057
## Max.cor.Y.rcv.1X1###glmnet             0.5485327       0.5505203
## Max.cor.Y##rcv#rpart                   0.5440181       0.5511143
## Random###myrandom_classfr              0.5349887       0.5054791
## MFO###myMFO_classfr                    0.5349887       0.5000000
## Final.All.X###glmnet                          NA              NA
##                                 max.AUCpROC.OOB min.elapsedtime.everything
## All.X##rcv#glmnet                     0.5514420                      9.352
## Low.cor.X##rcv#glmnet                 0.5447954                      8.077
## Interact.High.cor.Y##rcv#glmnet       0.5682479                      2.698
## Max.cor.Y.rcv.1X1###glmnet            0.5471714                      0.796
## Max.cor.Y##rcv#rpart                  0.5471714                      1.403
## Random###myrandom_classfr             0.5555897                      0.301
## MFO###myMFO_classfr                   0.5000000                      0.455
## Final.All.X###glmnet                         NA                      1.814
##                                 max.Accuracy.fit opt.prob.threshold.fit
## All.X##rcv#glmnet                      0.5764853                   0.50
## Low.cor.X##rcv#glmnet                  0.5764843                   0.45
## Interact.High.cor.Y##rcv#glmnet        0.5540889                   0.50
## Max.cor.Y.rcv.1X1###glmnet             0.5588742                   0.50
## Max.cor.Y##rcv#rpart                   0.5588680                   0.50
## Random###myrandom_classfr              0.5364733                   0.55
## MFO###myMFO_classfr                    0.5364733                   0.50
## Final.All.X###glmnet                   0.6062271                   0.45
##                                 opt.prob.threshold.OOB
## All.X##rcv#glmnet                                 0.45
## Low.cor.X##rcv#glmnet                             0.45
## Interact.High.cor.Y##rcv#glmnet                   0.50
## Max.cor.Y.rcv.1X1###glmnet                        0.55
## Max.cor.Y##rcv#rpart                              0.50
## Random###myrandom_classfr                         0.55
## MFO###myMFO_classfr                               0.50
## Final.All.X###glmnet                                NA
## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
##          Prediction
## Reference   D   R
##         D 115 122
##         R  56 150
##     err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKy        13.96326        1.064814        15.21644              NA
## N          99.87826       24.374964       124.73019              NA
## MKn        89.83789       19.844175       109.30718              NA
## SKn       369.05678      107.440151       477.62028              NA
## PKn        24.99773        5.146900        30.96337              NA
## SKy        22.63033       10.753683        33.59784              NA
## MKy       205.43862       45.180345       250.97436              NA
##     .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## PKy     0.01608271    0.004514673    0.003656307     28        2       NA
## N       0.12004595    0.108352144    0.107861060    209       24       35
## MKn     0.10798392    0.090293454    0.089579525    188       24       25
## SKn     0.44859276    0.501128668    0.504570384    781      122      154
## PKn     0.03216542    0.024830700    0.021937843     56        8        4
## SKy     0.02699598    0.051918736    0.051188300     47       12       16
## MKy     0.24813326    0.218961625    0.221206581    432       44       77
##     .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKy      2       12       18      2     28      2     30        0.5324071
## N       48      131      126     59    209     59    257        0.5078118
## MKn     40      124      104     49    188     49    228        0.4961044
## SKn    222      561      442    276    781    276   1003        0.4839646
## PKn     11       46       21     12     56     12     67        0.4679000
## SKy     23       37       33     28     47     28     70        0.4675514
## MKy     97      260      269    121    432    121    529        0.4657767
##     err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKy        0.4986880               NA        0.5072145
## N          0.4778864               NA        0.4853315
## MKn        0.4778611               NA        0.4794175
## SKn        0.4725439               NA        0.4761917
## PKn        0.4463880               NA        0.4621399
## SKy        0.4814964               NA        0.4799691
## MKy        0.4755524               NA        0.4744317
##  err.abs.fit.sum  err.abs.OOB.sum  err.abs.trn.sum  err.abs.new.sum 
##       825.802871       213.805031      1042.409669               NA 
##   .freqRatio.Fit   .freqRatio.OOB   .freqRatio.Tst           .n.Fit 
##         1.000000         1.000000         1.000000      1741.000000 
##         .n.New.D         .n.New.R           .n.OOB         .n.Trn.D 
##       236.000000               NA       443.000000      1171.000000 
##         .n.Trn.R           .n.Tst           .n.fit           .n.new 
##      1013.000000       547.000000      1741.000000       547.000000 
##           .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean 
##      2184.000000         3.421516         3.330416               NA 
## err.abs.trn.mean 
##         3.364696
## [1] "Features Importance for selected models:"
##                              All.X..rcv.glmnet.imp
## Q113181.fctrYes                         100.000000
## Q101163.fctrMom                          88.909843
## Q115611.fctrYes                          82.633375
## Gender.fctrM                             45.650319
## Q98197.fctrNo                            41.373950
## Hhold.fctrN:.clusterid.fctr4             39.716837
## Q115611.fctrNo                           37.044781
## Q120650.fctrNo                           33.982051
## Hhold.fctrPKn                            33.289229
## Q119851.fctrNo                           29.534974
## Q108950.fctrRisk-friendly                27.447610
## Q102674.fctrYes                          26.308431
## Q114386.fctrTMI                          26.076000
## Q116441.fctrYes                          23.933204
## Q98869.fctrNo                            23.495876
## Q106389.fctrNo                           20.789172
## Edn.fctr^4                               19.853090
## Q111848.fctrYes                          18.354110
## Q116441.fctrNo                           18.190177
## Q109367.fctrYes                          18.038294
## YOB.Age.fctr^8                           17.576007
## Q100562.fctrNo                           15.746827
## Q116601.fctrNo                           14.611496
## Q120379.fctrNo                           12.253336
## Q113583.fctrTunes                         8.026622
## Edn.fctr.L                                6.314399
## Q99480.fctrYes                            3.276618
## Q113181.fctrNo                            0.000000
## Q115195.fctrNo                            0.000000
## Q98197.fctrYes                            0.000000
##                              Final.All.X...glmnet.imp
## Q113181.fctrYes                             75.707527
## Q101163.fctrMom                            100.000000
## Q115611.fctrYes                             81.964527
## Gender.fctrM                                58.878689
## Q98197.fctrNo                               87.253169
## Hhold.fctrN:.clusterid.fctr4                26.820910
## Q115611.fctrNo                              53.011370
## Q120650.fctrNo                              34.354616
## Hhold.fctrPKn                                6.544867
## Q119851.fctrNo                              35.805767
## Q108950.fctrRisk-friendly                    2.947049
## Q102674.fctrYes                             34.422765
## Q114386.fctrTMI                              1.596116
## Q116441.fctrYes                             13.171454
## Q98869.fctrNo                               30.635897
## Q106389.fctrNo                              30.556692
## Edn.fctr^4                                  13.897773
## Q111848.fctrYes                              0.000000
## Q116441.fctrNo                              13.148996
## Q109367.fctrYes                              0.000000
## YOB.Age.fctr^8                               0.000000
## Q100562.fctrNo                              19.452739
## Q116601.fctrNo                              84.167268
## Q120379.fctrNo                               5.367857
## Q113583.fctrTunes                           30.749930
## Edn.fctr.L                                  12.721394
## Q99480.fctrYes                              14.892812
## Q113181.fctrNo                              15.133682
## Q115195.fctrNo                              14.143119
## Q98197.fctrYes                              12.664573
## [1] "glbObsNew prediction stats:"
## 
##   D   R 
## 236 311
##                  label step_major step_minor label_minor    bgn    end
## 6     predict.data.new          3          0           0 34.253 44.705
## 7 display.session.info          4          0           0 44.705     NA
##   elapsed
## 6  10.452
## 7      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##               label step_major step_minor label_minor    bgn    end
## 4 fit.data.training          2          0           0 17.276 27.741
## 6  predict.data.new          3          0           0 34.253 44.705
## 5 fit.data.training          2          1           1 27.742 34.252
## 2        fit.models          1          1           1  9.421 14.882
## 1      fit.models_1          1          0           0  5.813  9.421
## 3        fit.models          1          2           2 14.883 17.275
##   elapsed duration
## 4  10.466   10.465
## 6  10.452   10.452
## 5   6.511    6.510
## 2   5.461    5.461
## 1   3.608    3.608
## 3   2.393    2.392
## [1] "Total Elapsed Time: 44.705 secs"